Fiber remains the gold standard for high-speed broadband connectivity, but a T-Mobile US data center executive noted that 5G-based advanced wireless technologies have put pressure on fiber to keep pace.

“5G is so much faster and uses so much less overhead that I think the pressure is on fiber to catch up because we can do so much more that just adds more layers of demand on the fiber,” John Coster, manager for innovation, planning, and strategy at T-Mobile US said during a panel discussion at this week’s Yotta 2025 event.

“There’s a lot of pressure on the fiber to come up with better ways of handling, because we have more data, 5G can handle so much more data. I think that's the pressure on the transport side.”

Fiber network providers and vendors are working diligently to increase the capacity of their deployments, with much of that focus tied to an expected surge in demand being driven by AI applications. These advances are increasingly looking to increase overall network speeds and reduce latency to support AI-powered use cases.

Telecom operators like T-Mobile US are also growing their own fiber footprints. Over the past year, the carrier closed joint ventures to acquire Lumos and MetroNet, two deals that provide T-Mobile US with a fiber network passing up to 15 million locations.

The carrier’s initial push for those fiber assets are toward commercial services running from those networks, but the carrier has talked in the past about using acquired fiber assets to better manage backhaul costs associated with expanded customer data usage.

Coster explained that AI is ramping up that need.

“Think about 125 miles as a millisecond, and you’ve got like 30 miles between towers, so think about 100 miles from tower to tower from the fiber standpoint,” Coster said, adding in the roundtrip for data traveling over that fiber network. “We shoot for 10 milliseconds [of latency] and there are people that say with [augmented reality], virtual reality, you want to see less than three [seconds of latency].”

Coster said T-Mobile US works with application developers, like autonomous vehicles, to work through this latency deficit by fine-tuning the ability to process data within a vehicle and for what data needs to be sent back and forth over a fiber network.

“There's so many unknowns right now, it's just evolving and everyone's kind of trying to feel their way along,” Coster said. “How much do we invest in Edge AI to handle the kinds of traffic when we don't know who the client is? It's a little bit of an inventive process.”

Power to the Edge

Despite the ongoing challenge, telecom operators are looking to tap their expanded fiber footprints to bolster Edge deployments.

Wireless telecom operators are viewed as being in a prime position to support Edge data center deployments due to the geographical extent of their network architectures. Each operator typically has tens of thousands of cell sites deployed, with each having some level of power, connectivity, and footprint that can support equipment.

Verizon CEO Hans Vestberg this week wistfully touted that opportunity during an investor conference.

“We built the whole metro network and the Edge capabilities five years ago, way early maybe, but that's really what we want to see when AI will start to have devices that want to connect with the network in a totally different way,” Vestberg told an audience at the Goldman Sachs Communacopia + Technology Conference. “They're probably a couple of years out, but that is the next boundary of wireless growth for us. We're both going to have new offerings for these type of devices, and of course the manufacturer of the device needs to have Edge capabilities from us that we can charge as well.”

Verizon late last year signed a deal with Nvidia to power enterprise AI services and digital transformation efforts running over the carrier’s 5G private network and mobile Edge compute (MEC) infrastructure. Adam Koeppe, SVP of technology planning at Verizon, in a recent interview with SDxCentral, touted the carrier’s architecture and its ability to support AI-derived use cases.

“I think it’s really important to look at what these capabilities exist within the architecture, and how can AI, true AI, augment things that are already being done or create things that are brand new,” Koeppe said. “Where I see our evolution occurring is when you have an advanced cloud platform, as we do, you have an orchestration layer on top that we already have, and you then find ways to incorporate new AI capabilities on top of that. That’s going to allow your engineers and your operators to interface differently.”

These new AI interface opportunities are expected to push further Edge investments.

“As the focus of AI shifts from training to inference, Edge computing will be required to address the need for reduced latency and enhanced privacy,” Dave McCarthy, research VP for cloud and Edge services at IDC, wrote in a report last year. “This trend not only optimizes operation efficiencies but also fosters new business models that were previously not possible with centralized infrastructure. Distributing applications and data to Edge locations enables faster decision-making with reduced network congestion.”

However, T-Mobile US’s Coster explained that power availability could limit that potential.

“Our typical cell location, we're limited by how much power can be brought there,” Coster said, noting that in some cases it’s only five kilowatts (kW) to 10 kW. “Those [cell sites] are our biggest power bill by far in aggregate, but the ability to get energy to those spots is a big challenge. When I sit there and go, ‘well, can I stick a one-megawatt (MW) circuit?’”

Coster did add that with potential advances like quantum computing, “we’ll be able to do all kinds of magical stuff, … but right now, as it sits, if we want to put any kind of compute power at the end, we're going to be power limited.”